High Allocation Rate

Allocation rate is a term used when communicating the amount of memory allocated per time unit. Often it is expressed in MB/sec, but you can use PB/year if you feel like it. So that is all there is – no magic, just the amount of memory you allocate in your Java code measured over a period of time.

An excessively high allocation rate can mean trouble for your application’s performance. When running on a JVM, the problem will be revealed by garbage collection posing a large overhead.

How to Measure Allocation Rate?

One way to measure the allocation rate is to turn on GC logging by specifying -XX:+PrintGCDetails -XX:+PrintGCTimeStamps flags for the JVM. The JVM now starts logging the GC pauses similar to the following:

From the GC log above, we can calculate the allocation rate as the difference between the sizes of the young generation after the completion of the last collection and before the start of the next one. Using the example above, we can extract the following information:

At 291 ms after the JVM was launched, 33,280 K of objects were created. The first minor GC event cleaned the young generation, after which there were 5,088 K of objects in the young generation left.

At 446 ms after launch, the young generation occupancy had grown to 38,368 K, triggering the next GC, which managed to reduce the young generation occupancy to 5,120 K.

At 829 ms after the launch, the size of the young generation was 71,680 K and the GC reduced it again to 5,120 K.

This data can then be expressed in the following table calculating the allocation rate as deltas of the young occupancy:

Event

Time

Young before

Young after

Allocated during

Allocation rate

1st GC

291ms

33,280KB

5,088KB

33,280KB

114MB/sec

2nd GC

446ms

38,368KB

5,120KB

33,280KB

215MB/sec

3rd GC

829ms

71,680KB

5,120KB

66,560KB

174MB/sec

Total

829ms

N/A

N/A

133,120KB

161MB/sec

Having this information allows us to say that this particular piece of software had the allocation rate of 161 MB/sec during the period of measurement.

Why Should I Care?

After measuring the allocation rate we can understand how the changes in allocation rate affect application throughput by increasing or reducing the frequency of GC pauses. First and foremost, you should notice that only minor GC pauses cleaning the young generation are affected. Neither the frequency nor duration of the GC pauses cleaning the old generation are directly impacted by the allocation rate, but instead by the promotion rate, a term that we will cover separately in the next section.

Knowing that we can focus only on Minor GC pauses, we should next look into the different memory pools inside the young generation. As the allocation takes place in Eden, we can immediately look into how sizing Eden can impact the allocation rate. So we can hypothesize that increasing the size of Eden will reduce the frequency of minor GC pauses and thus allow the application to sustain faster allocation rates.

And indeed, when running the same application with different Eden sizes using -XX:NewSize -XX:MaxNewSize & -XX:SurvivorRatio parameters, we can see a two-fold difference in allocation rates.

If you are still wondering how this can be true – if you stop your application threads for GC less frequently you can do more useful work. More useful work also happens to create more objects, thus supporting the increased allocation rate.

Now, before you jump to the conclusion that “bigger Eden is better”, you should notice that the allocation rate might and probably does not directly correlate with the actual throughput of your application. It is a technical measurement contributing to throughput. The allocation rate can and will have an impact on how frequently your minor GC pauses stop application threads, but to see the overall impact, you also need to take into account major GC pauses and measure throughput not in MB/sec but in the business operations your application provides.

Give me an Example

Meet the demo application. Suppose that it works with an external sensor that provides a number. The application continuously updates the value of the sensor in a dedicated thread (to a random value, in this example), and from other threads sometimes uses the most recent value to do something meaningful with it in the processSensorValue() method:

As the name of the class suggests, the problem here is boxing. Possibly to accommodate the null check, the author made the sensorValue field a capital-D Double. This example is quite a common pattern of dealing with calculations based on the most recent value, when obtaining this value is an expensive operation. And in the real world, it is usually much more expensive than just getting a random value. Thus, one thread continuously generates new values, and the calculating thread uses them, avoiding the expensive retrieval.

The demo application is impacted by the GC not keeping up with the allocation rate. The ways to verify and solve the issue are given in the next sections.

Could my JVMs be Affected?

First and foremost, you should only be worried if the throughput of your application starts to decrease. As the application is creating too much objects that are almost immediately discarded, the frequency of minor GC pauses surges. Under enough of a load this can result in GC having a significant impact on throughput.

When you run into a situation like this, you would be facing a log file similar to the following short snippet extracted from the GC logs of the demo application introduced in the previous section. The application was launched as with the -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xmx32m command line arguments:

What should immediately grab your attention is the frequency ofminor GC events. This indicates that there are lots and lots of objects being allocated. Additionally, the post-GC occupancy of the young generation remains low, and no full collections are happening. These symptoms indicate that the GC is having significant impact to the throughput of the application at hand.

What is the Solution?

In some cases, reducing the impact of high allocation rates can be as easy as increasing the size of the young generation. Doing so will not reduce the allocation rate itself, but will result in less frequent collections. The benefit of the approach kicks in when there will be only a few survivors every time. As the duration of a minor GC pause is impacted by the number of surviving objects, they will not noticeably increase here.

The result is visible when we run the very same demo application with increased heap size and, with it, the young generation size, by using the -Xmx64m parameter:

However, just throwing more memory at it is not always a viable solution. Equipped with the knowledge on allocation profilers from the previous chapter, we may find out where most of the garbage is produced. Specifically, in this case, 99% are Doubles that are created with the readSensor method. As a simple optimization, the object can be replaced with a primitive double, and the null can be replaced with Double.NaN. Since primitive values are not actually objects, no garbage is produced, and there is nothing to collect. Instead of allocating a new object on the heap, a field in an existing object is directly overwritten.

The simple change (diff) will, in the demo application, almost completely remove GC pauses. In some cases, the JVM may be clever enough to remove excessive allocations itself by using the escape analysis technique. To cut a long story short, the JIT compiler may in some cases prove that a created object never “escapes” the scope it is created in. In such cases, there is no actual need to allocate it on the heap and produce garbage this way, so the JIT compiler does just that: it eliminates the allocation. See this benchmark for an example.